ReviewEuropean radiology2025
Large language models for structured reporting in radiology: past, present, and future.
Review in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
69 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effectiveness, Safety, and Workflow Burden of Large Language Model-Based Medical Report Generation: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Evaluation of a Retrieval-Augmented Generation-Powered Chatbot for Pre-CT Informed Consent: a Prospective Comparative Study.Journal of imaging informatics in medicine · 2025Trial
- Multidisciplinary attitudes towards LLM-structured oncologic CT reports: a prospective fixed-sequence questionnaire study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Transforming adnexal mass assessment: how large language model improve ovarian-adnexal reporting and data system interpretation and sonographer performance.Abdominal radiology (New York) · 2026Article
- How to apply artificial intelligence (AI) to facilitate and enhance MASH trials.Hepatology international · 2026Review
- Human-AI collaboration using GPT-5 for MR grading of endolymphatic hydrops in Ménière's disease.European radiology · 2026Article
- From zero-shot to fine-tuning: optimize large language models for error detection of ultrasound reports.Insights into imaging · 2026Article
- Less Is More: Radiologists Prefer Brief Narratives in LLM-Generated Clinical History Summaries.Journal of imaging informatics in medicine · 2026Article
- Radiology reporting in the age of artificial intelligence: implications for patient safety.BMJ quality & safety · 2026Article
- Automatic extraction of structured information from brain MRI reports using an open-weight large language model.European radiology · 2026Article
- Zero-shot segmentation and tumor diameter measurement of soft tissue sarcomas on MRI using a multimodal large language model.Skeletal radiology · 2026Article
- Large Language Model Translation of BI-RADS Breast Imaging Reports into Arabic: A Blinded Expert Evaluation of Diagnostic Communication Safety.Diagnostics (Basel, Switzerland) · 2026Article
- Structured reporting in knee MRI.Skeletal radiology · 2026Review
- [Keeping track of things: large language models for patient synopses : Source-bound system for clinical information systems].Radiologie (Heidelberg, Germany) · 2026Review
- Pre-Imaging Clinical Factors Associated With Cardiac MR Image Quality Using Large Language Model-Enabled Data Extraction.Journal of magnetic resonance imaging : JMRI · 2026Article
- AI-Assisted Brain Tumor MRI Reporting and Treatment-Planning Segmentation: A Retrospective Paired Workflow Evaluation.Biomedicines · 2026Article
- Development and Clinical Evaluation of a Large Language Model-Based System for Generating Patient-Friendly Echocardiography Reports: Two-Stage Retrospective Validation and Prospective Survey Study.Journal of medical Internet research · 2026Article
- Article
- Reasoning Model-Assisted Second-Reader Quality Control of Chinese-Language Ultrasound Reports: A Retrospective Imaging Informatics Study.Journal of imaging informatics in medicine · 2026Article
- GPT-4.1 and Llama 3.3 70 fail to detect clinically relevant errors in radiology reports in zero-shot evaluation.European radiology · 2026Article
9 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Structured reporting (SR) has long been a goal in radiology to standardize and improve the quality of radiology reports. Despite evidence that SR reduces errors, enhances comprehensiveness, and increases adherence to guidelines, its widespread adoption has been limited. Recently, large language models (LLMs) have emerged as a promising solution to automate and facilitate SR. Therefore, this narrative review aims to provide an overview of LLMs for SR in radiology and beyond. We found that the current literature on LLMs for SR is limited, comprising ten studies on the generative pre-trained transformer (GPT)-3.5 (n = 5) and/or GPT-4 (n = 8), while two studies additionally examined the performance of Perplexity and Bing Chat or IT5. All studies reported promising results and acknowledged the potential of LLMs for SR, with six out of ten studies demonstrating the feasibility of multilingual applications. Building upon these findings, we discuss limitations, regulatory challenges, and further applications of LLMs in radiology report processing, encompassing four main areas: documentation, translation and summarization, clinical evaluation, and data mining. In conclusion, this review underscores the transformative potential of LLMs to improve efficiency and accuracy in SR and radiology report processing. KEY POINTS: Question How can LLMs help make SR in radiology more ubiquitous? Findings Current literature leveraging LLMs for SR is sparse but shows promising results, including the feasibility of multilingual applications. Clinical relevance LLMs have the potential to transform radiology report processing and enable the widespread adoption of SR. However, their future role in clinical practice depends on overcoming current limitations and regulatory challenges, including opaque algorithms and training data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.